A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features

•Proposed methodology is based on flexible analytic wavelet transform (FAWT) for detection of Parkinson’s disease where EEG data is collected from two different centers.•The ninteen entropy-based features are extracted from sub-bands obtained after FAWT.•Relevant features are ranked using analysis of variance (ANOVA) to achieve accurate classification of Parkinson’s disease using k-nearest neighbor classifier.•The effectiveness of proposed approach is evaluated accurately in real-time with optimum computation time. Parkinson's disease (PD), a neurodegenerative disorder characterized by rest tremors, muscular rigidity, and bradykinesia, has become a global health concern. Currently, a neurologist determines the diagnosis of Parkinson's disease by taking into account several factors. An automated decision-making system would enhance patient care and improve the outcomes for the patient. Biomarkers, such as electroencephalograms (EEGs), can aid in the diagnosis process as they have proven useful in detecting abnormalities in the brain. This study presents a novel algorithm for the automated diagnosis of Parkinson's disease from EEG signals using a flexible analytic wavelet transform (FAWT). First, these acquired EEG signals are preprocessed before decomposition into five frequency sub-bands based on the FAWT method. Several entropy parameters are computed from the decomposed sub-bands and ranked based on their significance level in detecting PD through analysis of variance (ANOVA). Various classifiers are used to identify appropriate feature sets, including support vector machines (SVM), logistics, random forests (RF), radial basis functions (RBF), and k-nearest neighbors (KNN). The proposed approach is evaluated using data collected from two centers in Malaysia (Dataset-I) and the United States (Dataset-II). In dataset-I, the KNN classifier produces accuracy, specificity, sensitivity, and area under the curve of 99%, 99.45%, 99.12%, and 0.991, respectively, while in dataset-II, these values are 95.85%, 95.88%, 96.14%, and 0.959. The proposed system would be extremely useful for neurologists during their diagnostic process, as well as for current clinical practices.